On gradient-based learning in continuous games E Mazumdar, LJ Ratliff, SS Sastry SIAM Journal on Mathematics of Data Science 2 (1), 103-131, 2020 | 218* | 2020 |
On finding local nash equilibria (and only local nash equilibria) in zero-sum games EV Mazumdar, MI Jordan, SS Sastry arXiv preprint arXiv:1901.00838, 2019 | 163 | 2019 |
Feedback linearization for uncertain systems via reinforcement learning T Westenbroek, D Fridovich-Keil, E Mazumdar, S Arora, V Prabhu, ... 2020 IEEE international conference on robotics and automation (ICRA), 1364-1371, 2020 | 73* | 2020 |
Who leads and who follows in strategic classification? T Zrnic, E Mazumdar, S Sastry, M Jordan Advances in Neural Information Processing Systems 34, 15257-15269, 2021 | 67 | 2021 |
Mathematical framework for activity-based cancer biomarkers GA Kwong, JS Dudani, E Carrodeguas, EV Mazumdar, SM Zekavat, ... Proceedings of the National Academy of Sciences 112 (41), 12627-12632, 2015 | 63 | 2015 |
On approximate Thompson sampling with Langevin algorithms E Mazumdar, A Pacchiano, Y Ma, M Jordan, P Bartlett international conference on machine learning, 6797-6807, 2020 | 58* | 2020 |
Policy-Gradient Algorithms Have No Guarantees of Convergence in Linear Quadratic Games E Mazumdar, LJ Ratliff, MI Jordan, SS Sastry arXiv preprint arXiv:1907.03712, 2019 | 56* | 2019 |
Gradient-based inverse risk-sensitive reinforcement learning E Mazumdar, LJ Ratliff, T Fiez, SS Sastry 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5796-5801, 2017 | 54* | 2017 |
Convergence Guarantees for Gradient-Based Learning in Continuous Games. B Chasnov, LJ Ratliff, E Mazumdar, S Burden Uncertainty in artificial intelligence, 2019 | 50* | 2019 |
Langevin monte carlo for contextual bandits P Xu, H Zheng, EV Mazumdar, K Azizzadenesheli, A Anandkumar International Conference on Machine Learning, 24830-24850, 2022 | 42 | 2022 |
Global convergence to local minmax equilibrium in classes of nonconvex zero-sum games T Fiez, L Ratliff, E Mazumdar, E Faulkner, A Narang Advances in Neural Information Processing Systems 34, 29049-29063, 2021 | 36 | 2021 |
Fast distributionally robust learning with variance-reduced min-max optimization Y Yu, T Lin, EV Mazumdar, M Jordan International Conference on Artificial Intelligence and Statistics, 1219-1250, 2022 | 34 | 2022 |
Algorithmic collective action in machine learning M Hardt, E Mazumdar, C Mendler-Dünner, T Zrnic International Conference on Machine Learning, 12570-12586, 2023 | 23 | 2023 |
Zeroth-order methods for convex-concave min-max problems: Applications to decision-dependent risk minimization C Maheshwari, CY Chiu, E Mazumdar, S Sastry, L Ratliff International Conference on Artificial Intelligence and Statistics, 6702-6734, 2022 | 22 | 2022 |
Decentralized, communication-and coordination-free learning in structured matching markets C Maheshwari, S Sastry, E Mazumdar Advances in Neural Information Processing Systems 35, 15081-15092, 2022 | 18 | 2022 |
To observe or not to observe: Queuing game framework for urban parking LJ Ratliff, C Dowling, E Mazumdar, B Zhang 2016 IEEE 55th Conference on Decision and Control (CDC), 5286-5291, 2016 | 18 | 2016 |
Understanding the impact of parking on urban mobility via routing games on queue-flow networks D Calderone, E Mazumdar, LJ Ratliff, SS Sastry 2016 IEEE 55th Conference on Decision and Control (CDC), 7605-7610, 2016 | 15 | 2016 |
Convergent first-order methods for bi-level optimization and stackelberg games C Maheshwari, SS Sasty, L Ratliff, E Mazumdar arXiv preprint arXiv:2302.01421, 2023 | 14* | 2023 |
Sample-efficient robust multi-agent reinforcement learning in the face of environmental uncertainty L Shi, E Mazumdar, Y Chi, A Wierman arXiv preprint arXiv:2404.18909, 2024 | 13 | 2024 |
A finite-sample analysis of payoff-based independent learning in zero-sum stochastic games Z Chen, K Zhang, E Mazumdar, A Ozdaglar, A Wierman Advances in Neural Information Processing Systems 36, 75826-75883, 2023 | 13 | 2023 |